Download Functional Programming Paradigm Learning Outcomes:

Functional Programming Paradigm
Learning Outcomes:
By the end of this module you will be able to :
1.
2.
Describe the main concepts of Functional Programming
Read, understand and write programs in one of the Functional
Programming Languages (Scheme)
15. Functional Programming Languages
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15-1
Chapter 15
Functional
Programming
Languages
ISBN 0-321-19362-8
Topics
• Introduction
• Mathematical Functions
• Fundamentals of Functional Programming
Languages
• Introduction to Scheme
• Applications of Functional Languages
• Comparison of Functional and Imperative
Languages
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15-3
Introduction
• The design of the imperative languages is
based directly on the von Neumann
architecture
– Efficiency is the primary concern, rather than the
suitability of the language for software
development
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15-4
Introduction
• The design of the functional languages is
based on mathematical functions
– A solid theoretical basis that is also closer to the
user, but relatively unconcerned with the
architecture of the machines on which programs
will run
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15-5
Mathematical Functions
• Def: A mathematical function is a mapping of
members of one set, called the domain set, to
another set, called the range set
• A lambda expression specifies the
parameter(s) and the mapping of a function in
the following form
(x) x * x * x
for the function cube (x) = x * x * x
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Mathematical Functions
• Lambda expressions describe nameless
functions
• Lambda expressions are applied to
parameter(s) by placing the parameter(s) after
the expression
e.g. ((x) x * x * x)(3)
which evaluates to 27
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15-7
Mathematical Functions
• Functional Forms
– Def: A higher-order function, or functional form,
is one that either takes functions as parameters or
yields a function as its result, or both
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Functional Forms
1. Function Composition
– A functional form that takes two functions as
parameters and yields a function whose value is
the first actual parameter function applied to the
application of the second
Form: h  f ° g
which means h (x)  f ( g ( x))
For f (x)  x * x * x and g (x)  x + 3,
h  f ° g yields (x + 3)* (x + 3)* (x + 3)
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Functional Forms
2. Construction
– A functional form that takes a list of functions as
parameters and yields a list of the results of
applying each of its parameter functions to a given
parameter
Form: [f, g]
For f (x)  x * x * x and g (x)  x + 3,
[f, g] (4) yields (64, 7)
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Functional Forms
3. Apply-to-all
– A functional form that takes a single function as a
parameter and yields a list of values obtained by
applying the given function to each element of a
list of parameters
Form: 
For h (x)  x * x * x
( h, (3, 2, 4)) yields (27, 8, 64)
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15-11
Fundamentals of Functional
Programming Languages
• The objective of the design of a FPL is to mimic
mathematical functions to the greatest extent possible
• The basic process of computation is fundamentally
different in a FPL than in an imperative language
– In an imperative language, operations are done and the
results are stored in variables for later use
– Management of variables is a constant concern and source of
complexity for imperative programming
• In an FPL, variables are not necessary, as is the case
in mathematics
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15-12
Fundamentals of Functional
Programming Languages
• In an FPL, the evaluation of a function always
produces the same result given the same
parameters
– This is called referential transparency
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15-13
Scheme Program: Example 1
www.htdp.org
Write a Scheme function to compute the area of a ring
;; Contract: area-of-ring : number number -> number
;; Purpose: to compute the area of a ring whose radii are given
(define (area-of-ring outer inner)
(- (* 3.14 (* outer outer))
(* 3.14 (* inner inner))))
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15-14
Scheme Program: Example 2
• Write a Scheme function to compute distance from
(0, 0) to a given point.
In general, we know from geometry that the
distance from the origin to a position with
coordinates x and y is distance  X2 + Y2
;;(make-posn 8, 6)
(define (distance-to-0 a-posn)
(sqrt
(+ (sqr (posn-x a-posn))
(sqr (posn-y a-posn)))))
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15-15
Introduction to Scheme
• A mid-1970s dialect of LISP, designed to be a
cleaner, more modern, and simpler version
than the contemporary dialects of LISP
• Uses only static scoping
• Functions are first-class entities
– They can be the values of expressions and
elements of lists
– They can be assigned to variables and passed as
parameters
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15-16
Introduction to Scheme
•
Primitive Functions
1. Arithmetic: +, -, *, /, ABS,
SQRT, REMAINDER, MIN, MAX
e.g., (+ 5 2) yields 7
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15-17
Introduction to Scheme
2. QUOTE -takes one parameter; returns the
parameter without evaluation
– QUOTE is required because the Scheme interpreter,
named EVAL, always evaluates parameters to
function applications before applying the function.
QUOTE is used to avoid parameter evaluation
when it is not appropriate
– QUOTE can be abbreviated with the apostrophe
prefix operator
e.g., '(A B) is equivalent to (QUOTE (A B))
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15-18
Introduction to Scheme
3. CAR takes a list parameter; returns the first
element of that list
e.g., (CAR '(A B C)) yields A
(CAR '((A B) C D)) yields (A B)
4. CDR takes a list parameter; returns the list
after removing its first element
e.g., (CDR '(A B C)) yields (B C)
(CDR '((A B) C D)) yields (C D)
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15-19
Introduction to Scheme
5. CONS takes two parameters, the first of
which can be either an atom or a list and the
second of which is a list; returns a new list
that includes the first parameter as its first
element and the second parameter as the
remainder of its result
e.g., (CONS 'A '(B C)) returns (A B C)
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15-20
Introduction to Scheme
6. LIST - takes any number of parameters;
returns a list with the parameters as elements
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15-21
Introduction to Scheme
• Lambda Expressions
– Form is based on  notation
e.g., (LAMBDA (L) (CAR (CAR L)))
L is called a bound variable
• Lambda expressions can be applied
e.g.,
((LAMBDA (L) (CAR (CAR L))) '((A B) C D))
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15-22
Introduction to Scheme
• A Function for Constructing Functions
DEFINE - Two forms:
1. To bind a symbol to an expression
e.g.,
(DEFINE pi 3.141593)
(DEFINE two_pi (* 2 pi))
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15-23
Introduction to Scheme
2. To bind names to lambda expressions
e.g.,
(DEFINE (cube x) (* x x x))
Example use:
(cube 4)
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15-24
Introduction to Scheme
• Evaluation process (for normal functions):
1. Parameters are evaluated, in no particular order
2. The values of the parameters are substituted into
the function body
3. The function body is evaluated
4. The value of the last expression in the body is the
value of the function
(Special forms use a different evaluation process)
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15-25
Introduction to Scheme
• Examples:
(DEFINE (square x) (* x x))
(DEFINE (hypotenuse side1 side1)
(SQRT (+ (square side1)
(square side2)))
)
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15-26
Introduction to Scheme
• Predicate Functions: (#T is true and ()is false)
1. EQ? takes two symbolic parameters; it returns #T if both
parameters are atoms and the two are the same
e.g., (EQ? 'A 'A) yields #T
(EQ? 'A '(A B)) yields ()
– Note that if EQ? is called with list parameters, the result
is not reliable
– Also, EQ? does not work for numeric atoms
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15-27
Introduction to Scheme
• Predicate Functions:
2. LIST? takes one parameter; it returns #T if the
parameter is a list; otherwise()
3. NULL? takes one parameter; it returns #T if the
parameter is the empty list; otherwise()
Note that NULL? returns #T if the parameter is()
4. Numeric Predicate Functions
=, <>, >, <, >=, <=, EVEN?, ODD?,
ZERO?, NEGATIVE?
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15-28
Introduction to Scheme
• Output Utility Functions:
(DISPLAY expression)
(NEWLINE)
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15-29
Introduction to Scheme
• Control Flow
1. Selection- the special form, IF
(IF predicate then_exp
else_exp)
e.g.,
(IF (<> count 0)
(/ sum count)
0
)
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15-30
Introduction to Scheme
• Control Flow
2. Multiple Selection - the special form, COND
General form:
(COND
(predicate_1 expr {expr})
(predicate_1 expr {expr})
...
(predicate_1 expr {expr})
(ELSE expr {expr})
)
Returns the value of the last expr in the first
pair whose predicate evaluates to true
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15-31
Example of COND
(DEFINE (compare x y)
(COND
((> x y) (DISPLAY “x is
greater than y”))
((< x y) (DISPLAY “y is
greater than x”))
(ELSE (DISPLAY “x and y are
equal”))
)
)
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15-32
Example Scheme Functions
1. member - takes an atom and a simple list;
returns #T if the atom is in the list; ()
otherwise
(DEFINE (member atm lis)
(COND
((NULL? lis) '())
((EQ? atm (CAR lis)) #T)
((ELSE (member atm (CDR
lis)))
))
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15-33
Example Scheme Functions
2. equalsimp - takes two simple lists as parameters;
returns #T if the two simple lists are equal; ()
otherwise
(DEFINE (equalsimp lis1 lis2)
(COND
((NULL? lis1) (NULL? lis2))
((NULL? lis2) '())
((EQ? (CAR lis1) (CAR lis2))
(equalsimp(CDR lis1)(CDR lis2)))
(ELSE '())
))
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15-34
Example Scheme Functions
3. equal - takes two general lists as parameters; returns #T
if the two lists are equal; ()otherwise
(DEFINE (equal lis1 lis2)
(COND
((NOT (LIST? lis1))(EQ? lis1 lis2))
((NOT (LIST? lis2)) '())
((NULL? lis1) (NULL? lis2))
((NULL? lis2) '())
((equal (CAR lis1) (CAR lis2))
(equal (CDR lis1) (CDR lis2)))
(ELSE '())
))
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15-35
Example Scheme Functions
4. append - takes two lists as parameters; returns the
first parameter list with the elements of the second
parameter list appended at the end
(DEFINE (append lis1 lis2)
(COND
((NULL? lis1) lis2)
(ELSE (CONS (CAR lis1)
(append (CDR lis1) lis2)))
))
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15-36
Introduction to Scheme
• The LET function
– General form:
(LET (
(name_1 expression_1)
(name_2 expression_2)
...
(name_n expression_n))
body
)
– Semantics: Evaluate all expressions, then bind the values to
the names; evaluate the body
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15-37
Introduction to Scheme
(DEFINE (quadratic_roots a b c)
(LET (
(root_part_over_2a
(/ (SQRT (- (* b b) (* 4 a c)))
(* 2 a)))
(minus_b_over_2a (/ (- 0 b) (* 2 a)))
(DISPLAY (+ minus_b_over_2a
root_part_over_2a))
(NEWLINE)
(DISPLAY (- minus_b_over_2a
root_part_over_2a))
))
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15-38
Introduction to Scheme
• Functional Forms
1. Composition
- The previous examples have used it
2. Apply to All - one form in Scheme is mapcar
- Applies the given function to all elements of the given list;
result is a list of the results
(DEFINE (mapcar fun lis)
(COND
((NULL? lis) '())
(ELSE (CONS (fun (CAR lis))
(mapcar fun (CDR lis))))
))
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15-39
Introduction to Scheme
• It is possible in Scheme to define a function that builds
Scheme code and requests its interpretation
• This is possible because the interpreter is a useravailable function, EVAL
• e.g., suppose we have a list of numbers that must be
added together
(DEFINE (adder lis)
(COND
((NULL? lis) 0)
(ELSE (+ (CAR lis)
(adder(CDR lis ))))
))
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15-40
Adding a List of Numbers
((DEFINE (adder lis)
(COND
((NULL? lis) 0)
(ELSE (EVAL (CONS '+ lis)))
))
• The parameter is a list of numbers to be
added; adder inserts a + operator and
interprets the resulting list
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15-41
Introduction to Scheme
• Scheme includes some imperative features:
1. SET! binds or rebinds a value to a name
2. SET-CAR! replaces the car of a list
3. SET-CDR! replaces the cdr part of a list
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15-42
A Scheme Program Example
• Problem
Newspapers often contain exercises that ask readers to find all possible
words made up from some letters. One way to play this game is to form all
possible arrangements of the letters in a systematic manner and to see
which arrangements are dictionary words. Suppose the letters ``a,'' ``d,''
``e,'' and ``r'' are given. There are twenty-four possible arrangements of
these letters:
ader daer dear dera aedr eadr edar edra
aerd eard erad erda adre dare drae drea
arde rade rdae rdea ared raed read reda
• The three legitimate words in this list are ``read,'' ``dear,'' and ``dare.''
• The systematic enumeration of all possible arrangements is clearly a task
for a computer program.
• It consumes a word and produces a list of the word's letter-by-letter
rearrangements.
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15-43
A Scheme Program Example
•
Analysis of the problem
One representation of a word is a list of symbols.
Each item in the input represents a letter: 'a, 'b, ..., 'z.
Here is the data definition for words:
A word is either
empty, or
(cons a w)
where a is a symbol ('a, 'b, ..., 'z) and w is a word.
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15-44
A Scheme Program Example
;; arrangements : word -> list-of-words
;; to create a list of all rearrangements of the letters in
a-word
(define a-letter ‘d)
(define (insert-everywhere/in-all-words a-letter)
(cons (cons 'e (cons 'r empty))
(cons (cons 'r (cons 'e empty)) empty)))
(define (arrangements a-word)
(cond [(empty? a-word) (cons empty empty)]
[else (insert-everywhere/in-all-words
(first a-word) (arrangements (rest a-word)))]))
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15-45
Applications of Functional
Languages
• APL is used for throw-away programs
• LISP is used for artificial intelligence
–
–
–
–
Knowledge representation
Machine learning
Natural language processing
Modeling of speech and vision
• Scheme is used to teach introductory
programming at a significant number of
universities
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15-46
Comparing Functional and
Imperative Languages
• Imperative Languages:
–
–
–
–
Efficient execution
Complex semantics
Complex syntax
Concurrency is programmer designed
• Functional Languages:
–
–
–
–
Simple semantics
Simple syntax
Inefficient execution
Programs can automatically be made concurrent
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15-47